""" CCT Bitcoin Crash Prediction Validation (V2) Passes full cumulative price history to properly detect sustained drawdowns """ import numpy as np import pandas as pd import yfinance as yf # ============================================================ # KNOWN REAL-WORLD BTC EVENTS (from research) # ============================================================ REAL_EVENTS = [ { "name": "October 2025 Tariff Flash Crash", "date_start": "2025-09-15", "date_end": "2025-10-15", "description": "ATH $126K Oct 6, then 100% China tariffs, $19B liquidations, BTC $126K→$110K", "severity": "HIGH" }, { "name": "October-November 2025 Bear Market", "date_start": "2025-10-01", "date_end": "2025-12-01", "description": "BTC falls below $90K, breaks 50-week MA, Death Cross Nov 16, $85K support breaks", "severity": "CRITICAL" }, { "name": "November 2025 Gov Shutdown Liquidity Drought", "date_start": "2025-10-15", "date_end": "2025-12-15", "description": "43-day gov shutdown, TGA freeze, ETF outflows $3B, FinCEN vs Huione, DATCo implosion", "severity": "CRITICAL" }, { "name": "February 2026 Crash to $60K", "date_start": "2026-01-15", "date_end": "2026-02-15", "description": "BTC drops 52% from ATH, plunges to $60K, $2B liquidations", "severity": "EXTREME" }, { "name": "April-June 2024 Bullish Rally", "date_start": "2024-04-08", "date_end": "2024-06-30", "description": "Post-ETF approval rally period, generally bullish", "severity": "BULLISH" }, { "name": "July-Sept 2025 ATH Run", "date_start": "2025-07-15", "date_end": "2025-09-01", "description": "BTC reaches ATH $124K+ on pro-crypto administration", "severity": "BULLISH" }, ] # ============================================================ # RUN CCT PREDICTIONS WITH FULL HISTORY # ============================================================ print("🔬 CCT Bitcoin Prediction Validation (V2 - Full History)") print("=" * 70) # Fetch BTC data print("\n📊 Fetching BTC-USD data (2y, 1d)...") stock = yf.Ticker("BTC-USD") df = stock.history(period="2y", interval="1d") prices = df['Close'].values dates = df.index print(f"✓ Retrieved {len(prices)} data points from {dates[0].strftime('%Y-%m-%d')} to {dates[-1].strftime('%Y-%m-%d')}") import sys sys.path.insert(0, '/home/per/Documents/Universe as a Fractal') from ex01 import CCT_MarketCrash_Predictor predictor = CCT_MarketCrash_Predictor( trained_theta=0.7342, collapse_threshold=0.7, critical_escape_prob=0.2, window_size=14, prediction_horizon=5 ) # Run predictions with FULL cumulative history passed to drawdown predictions = [] running_max = prices[0] for idx in range(predictor.WINDOW, len(prices)): # Standard sliding window for CCT components start_idx = max(0, idx - predictor.WINDOW) window = prices[start_idx:idx + 1] # Full cumulative history for drawdown full_history = prices[:idx + 1] psi_result = predictor.compute_red_star_potential(window) # Override drawdown risk with full-history calculation full_dd_risk = predictor._compute_drawdown_risk(full_history) # Recompute Psi_Red with full-history drawdown (exponential suppression) Psi_Red_corrected = psi_result['P_esc'] * psi_result['entropy_indicator'] * psi_result['energy_term'] * np.exp(-5.0 * full_dd_risk) prediction = 'RED_STAR' if Psi_Red_corrected > 0.004 else 'BLACK_HOLE' confidence = min(Psi_Red_corrected / 0.02, 1.0) if prediction == 'RED_STAR' else max(0.5, 1.0 - Psi_Red_corrected / 0.004) predictions.append({ 'index': idx, 'date': dates[idx], 'prediction': prediction, 'Psi_Red': Psi_Red_corrected, 'Psi_Red_original': psi_result['Psi_Red'], 'drawdown_risk': full_dd_risk, 'confidence': confidence, 'price': prices[idx] }) pred_df = pd.DataFrame(predictions) # ============================================================ # VALIDATE AGAINST REAL EVENTS # ============================================================ print("\n" + "=" * 70) print("📋 VALIDATION: CCT Predictions vs Real BTC Events") print("=" * 70) hits = 0 misses = 0 false_alarms = 0 for event in REAL_EVENTS: start = pd.Timestamp(event["date_start"], tz="UTC") end = pd.Timestamp(event["date_end"], tz="UTC") mask = (pred_df['date'] >= start) & (pred_df['date'] <= end) event_preds = pred_df[mask] if len(event_preds) == 0: print(f"\n⚠️ NO DATA for: {event['name']}") misses += 1 continue black_hole_count = len(event_preds[event_preds['prediction'] == 'BLACK_HOLE']) red_star_count = len(event_preds[event_preds['prediction'] == 'RED_STAR']) total = len(event_preds) bh_pct = black_hole_count / total * 100 avg_psi = event_preds['Psi_Red'].mean() avg_dd = event_preds['drawdown_risk'].mean() min_psi = event_preds['Psi_Red'].min() print(f"\n{'─' * 70}") print(f"📌 EVENT: {event['name']}") print(f" Period: {event['date_start']} to {event['date_end']}") print(f" Real event: {event['description']}") print(f" Severity: {event['severity']}") print(f" CCT Predictions: {total} days analyzed") print(f" Black Hole: {black_hole_count} ({bh_pct:.0f}%)") print(f" Red Star: {red_star_count} ({100-bh_pct:.0f}%)") print(f" Avg Ψ_Red: {avg_psi:.6f} | Avg DD Risk: {avg_dd:.1%}") print(f" Min Ψ_Red: {min_psi:.6f}") if event['severity'] in ['CRITICAL', 'EXTREME', 'HIGH']: if bh_pct > 50: print(f" ✅ HIT: CCT correctly predicted crash risk (BH: {bh_pct:.0f}%)") hits += 1 elif bh_pct > 15: print(f" ⚠️ PARTIAL: Mixed signal (BH: {bh_pct:.0f}%) - some warning") hits += 0.5 else: print(f" ❌ MISS: CCT failed to predict crash (BH: {bh_pct:.0f}%)") misses += 1 elif event['severity'] == 'BULLISH': if bh_pct < 30: print(f" ✅ HIT: CCT correctly showed stability (BH: {bh_pct:.0f}%)") hits += 1 elif bh_pct < 60: print(f" ⚠️ PARTIAL: Mixed signal during rally (BH: {bh_pct:.0f}%)") hits += 0.5 else: print(f" ❌ FALSE ALARM: CCT predicted crash during rally (BH: {bh_pct:.0f}%)") false_alarms += 1 # ============================================================ # OVERALL ACCURACY # ============================================================ total_events = len(REAL_EVENTS) print(f"\n{'=' * 70}") print(f"📊 OVERALL ACCURACY") print(f"{'=' * 70}") print(f" Total Events Tested: {total_events}") print(f" Hits: {hits}") print(f" Misses: {misses}") print(f" False Alarms: {false_alarms}") print(f" Accuracy: {hits/total_events*100:.1f}%") if (hits + misses) > 0: print(f" Crash Detection Rate: {hits/(hits+misses)*100:.1f}%") # ============================================================ # MONTHLY BREAKDOWN # ============================================================ print(f"\n{'=' * 70}") print(f"📅 MONTHLY BLACK HOLE BREAKDOWN") print(f"{'=' * 70}") pred_df['month'] = pred_df['date'].dt.to_period('M') monthly = pred_df.groupby('month').agg( total=('prediction', 'count'), bh_count=('prediction', lambda x: (x == 'BLACK_HOLE').sum()), avg_dd=('drawdown_risk', 'mean'), min_psi=('Psi_Red', 'min'), avg_price=('price', 'mean') ) monthly['bh_pct'] = monthly['bh_count'] / monthly['total'] * 100 for month, row in monthly.iterrows(): if row['bh_count'] > 0: print(f" {month}: {int(row['bh_count'])}/{int(row['total'])} BH ({row['bh_pct']:.0f}%) " f"| Avg DD: {row['avg_dd']:.1%} | Min Ψ: {row['min_psi']:.6f} | Avg Price: ${row['avg_price']:,.0f}")